Obtaining approval for a therapeutic product (e.g., a medical device, a pharmaceutical product such as a drug, etc.) requires a clinical trial in which the therapeutic product is tested on human subjects to validate the product's safety and efficacy for its intended purpose. To ensure that the results of the clinical trials are reliable and that results are reproducible, many clinical trials are often multi-site and/or multinational operations which typically require substantial planning and oversight to run efficiently. For example, a clinical trial may involve hundreds or thousands of patients recruited worldwide, and a central management service may be employed to manage various aspects of the clinical trial.
When planning a clinical trial, it is often desirable to estimate the amount of time necessary to recruit and enroll a predetermined number of patients on which the therapeutic product will be tested. The developer/manufacturer of the therapeutic product has a substantial economic interest in obtaining approval as quickly as possible to expedite return on their investment, thus often rendering accurate prediction of the clinical trial timelines essential. Conventional methods of estimating patient recruitment timelines for clinical trials typically consider only the number of sites and the desired number of patients to be recruited. A straight line extrapolation is then typically performed based on the number of patients expected to be recruited on a per site per month basis. However, such linear estimation models lead to inaccurate predictions of the number of patients that can be recruited in a given amount of time, and ultimately, underestimate the time it will take for a clinical trial to complete. By the time the problem is recognized, it may be too late to add sites or otherwise correct the problem. Thus, clinical trials planned using conventional methods may be at risk for taking longer to complete than predicted, often by substantial amounts.